Gradation Parameters Research Articles

Mesoscopic simulation of concrete drying shrinkage with hydration kinetics

Shrinkage-induced cracking significantly impacts the durability of mass concrete structures. Quantitatively evaluating drying shrinkage of concrete proves challenging due to the time-consuming experiments and overlooked microstructure changes during the hydration process. To address this concern, this study initially characterized the long-term hydration products and microstructure of low-heat Portland cement (LHPC) through microstructural experiments. Subsequently, a novel high-resolution mesoscale framework is developed to investigate the drying shrinkage with hydration kinetics. High-resolution models consist of realistic-shaped aggregates are validated by the aggregate morphology and gradation parameters of core sample from mass concrete. Concurrently, the quantitative effects of internal and external factors on LHPC drying shrinkage are explored. Results indicated that LHPC possesses a denser microstructure, lower porosity, higher carbonation resistance, and 20% lower drying shrinkage compared to moderate-heat Portland cement, suggesting promising applications. Furthermore, experimental and computational findings suggested that increasing aggregate volume, controlling aggregate morphology, and adjusting curing time and humidity could be employed to reduce and manage drying shrinkage, ensuring concrete structure durability.

The influence of material gradation parallel to two unequal mode-III cracks in functionally graded materials via strain gradient elasticity theory

In this article, the strain gradient elasticity (SGE) theory is used to investigate the mechanical behaviour of a functionally graded material (FGM) weakened by two unequal collinear mode-III cracks. The SGE theory uses two material characteristic lengths, ℓ and ℓ′, to account for volumetric and surface strain-gradient factors, respectively. The directions of both cracks coincide with the material gradation, which is oriented parallel to the x-axis. The numerical outcomes of the problem are obtained by constructing the system of equations using the hyper-singular integral-differential equation technique. The influence of the material gradation parameter on various fracture parameters, including the stress intensity factor (SIF), strain and crack surface displacement (CSD), is numerically shown. In addition, different loads are considered while analysing CSD profiles in connection to the gradation parameter β. Furthermore, the effect of inter-crack distance on CSD profiles is comprehensively explored, showing information between crack geometry and material gradation.

Assessing gradient parameters for damage control in notched plates: Finite element analysis of locally functionally graded materials using the Gurson-Tvergaard-needleman (GTN) model

This study investigates functionally graded materials (FGMs) as reinforcement for notched plates prone to crack propagation. Using a novel meshing technique and the Gurson-Tvergaard-Needleman damage model within ABAQUS-Explicit, we explore how various gradation parameters affect material damage under stress. The model, incorporating Von Mises stress theory and Voce hardening law, simulates plastic flow and progressive damage from void dynamics. Validated against experimental data, our findings reveal that local gradation parameters significantly influence void damage and structural performance of notched components. This research provides crucial insights into FGMs' potential to enhance structural integrity in engineering applications involving notched structures.

ANN-based evaluation system for erosion resistant highway shoulder rocks

Highway shoulder rocks are exposed to continuous erosion force due to extreme rainfall that could be caused by global warming to some extent. However, the logical design method for erosion-resistant highway shoulder is not well-researched yet. This study utilized a large-scale UNLETB (University of Nebraska Lincoln–Erosion Testing Bed) with a 7.6 cm nozzle width and a 4000 cm3/sec flow rate to study the erosion characteristics of highway shoulder rocks. Test results showed that different shoulder materials currently used had vastly diverse erosion resistance. However, the clear criteria between the erosion-resistant gradation and other gradation could not be determined easily. Then, this study trained ANN (Artificial Neural Network) with test results to conveniently distinguish the erosion resistance of rocks from other rocks. The ANN predicted the acceptable/non-acceptable erosion characteristics of shoulder rocks with close to 99% accuracy based on the three gradation parameters (D10, D30, and D60).

Repose angle prediction of railway ballast based on Hopper Flow Test and PCA-Stacking ensemble learning method

The deterioration of track ballast is mainly reflected in the fragmentation of ballast particles and the evolution of grading. The repose angle of the ballast is the internal friction angle, reflecting the shear strength of the track ballast. The angle of repose of 158 ballast samples with varying gradation parameters was tested by conducting hopper flow tests on ballast particles. Then, based on this database, a stacking ensemble regressor-based principal component analysis (PCA) was proposed for the repose angle prediction. The data are first processed by PCA and then randomized into the train (80%) and test (20%) data. Besides stacking ensemble regressor, five different individual regressors, including support vector regressor, k-nearest neighbors, random forest, gradient boosting decision tree, and adaptive boosting, are compared and analyzed. The results indicate that the stacking ensemble regressor performs better than single regressors and demonstrates enhanced robustness and generalization capabilities. Additionally, the Effect of the number of principal component indicators on the prediction accuracy is determined, and the difference in stacking ensemble prediction performance with and without PCA is discussed. Finally, a computer prediction system for ballast repose angle was developed to make the proposed stacking ensemble regressor directly applicable to the railroad construction site. This model can be used in practical track maintenance decisions, whereby the ballast gradation can facilitate rapid assessment of the angle of repose.

Intelligent prediction and evaluation method of optimal frequency based on PSO-BPNN-AdaBoost model

To achieve rapid and accurate determination of the optimal compaction frequency for high-speed railway subgrade materials, a method based on the PSO-BPNN-AdaBoost model for intelligent frequency estimation is proposed. Firstly, the Particle Swarm Optimization (PSO) algorithm is introduced to obtain the optimal hyperparameters of the Backpropagation Neural Network (BPNN), and then the PSO-BPNN-AdaBoost model is established by integrating the AdaBoost ensemble algorithm. Secondly, taking graded gravel fill material as an example, the Grey Relational Analysis algorithm (GRA) is employed to identify the main controlling features affecting f op as input features for the model, and the predictive performance of the model is evaluated. Finally, the model’s reliability is verified through ablation analysis. The results indicate that the PSO-BPNN-AdaBoost model demonstrates higher predictive accuracy. The main controlling features influencing f op are revealed to be the maximum particle size (d max), gradation parameters (b, m), coarse aggregate elongation index (EI), Los Angeles Abrasion (LAA), water absorption rates (W ac, W af).

Mechanical property enhancement in additively manufactured NiTi double-asymmetric honeycombs with bioinspired graded design

ABSTRACT The cuttlebone is known for its ability to possess high specific stiffness, progressive failure and lightweight from the porous chambered structure. Inspired by the microstructural characteristics of cuttlebone and incorporating the wall gradient design, a series of double-asymmetric honeycombs were designed and processed by LPBF. Results indicated that bionic structural units with the junction design can maintain the integrity of the residual parts after local buckling and failure, improving the load-bearing capacity. The double-asymmetric honeycomb with gradation parameter α = 2/3 achieved a maximum specific compressive strength of 70.64 MPa cm3/g. As α decreases, there is an increase in specific energy absorption and a narrowing of the hysteresis loop. The as-build honeycomb had undergone stress-induced martensite transformation during compression. The dissipated mechanical energy (ME) decreased with the increasing cycle number and the decreasing α. The results provide design guidelines and process strategies for developing high-performance honeycombs.

Study on Effect of Particle Size Distribution on Water-Retention Capacity of Coral Sand from Macro and Micro Perspective

The reclamation coral sand (CS) layer is the survival environment for island reef vegetation in the South China Sea. The root system within the CS bed draws water necessary for vegetation growth, implying that the water-retention capacity of CS plays a pivotal role in determining vegetation viability. Particle size distribution (PSD) significantly influences the water-retention capacity of geomaterials. This study examines the impact of PSD on the water-retention capacity of CS from both macro (soil–water characteristic curve, SWCC) and micro (pore water distribution) perspectives using the pressure plate test and nuclear magnetic resonance technique, and an F&X model was used to analyze the SWCC of CS. The findings indicated that the F&X model aptly describes the SWCC of CS with different PSDs. Both the air entry value and residual water content rise with an increased content of fine grains (d < 0.25 mm), suggesting that the presence of fine grains augments the water-retention capacity of CS. It is considered that a size range of d = 0.075–0.25 mm predominantly impacts the water-retention capacity of CS. The PSD primarily influences the water-retention capacity by affecting the pore size distribution of CS. The volume of small pores swells with the surge of fine-grain content, while the maximum pore size contracts with increasing fine-grain content. Limited pore connectivity in CS means macropores can retain water even under high suction, bolstering the water-retention capacity of CS. These findings offer theoretical guidance for selecting gradation parameters for the planting layer on island reefs.

A Framework for Determining the Optimal Vibratory Frequency of Graded Gravel Fillers Using Hammering Modal Approach and ANN.

To address the uncertainty of optimal vibratory frequency fov of high-speed railway graded gravel (HRGG) and achieve high-precision prediction of the fov, the following research was conducted. Firstly, commencing with vibratory compaction experiments and the hammering modal analysis method, the resonance frequency f0 of HRGG fillers, varying in compactness K, was initially determined. The correlation between f0 and fov was revealed through vibratory compaction experiments conducted at different vibratory frequencies. This correlation was established based on the compaction physical-mechanical properties of HRGG fillers, encompassing maximum dry density ρdmax, stiffness Krd, and bearing capacity coefficient K20. Secondly, the gray relational analysis algorithm was used to determine the key feature influencing the fov based on the quantified relationship between the filler feature and fov. Finally, the key features influencing the fov were used as input parameters to establish the artificial neural network prediction model (ANN-PM) for fov. The predictive performance of ANN-PM was evaluated from the ablation study, prediction accuracy, and prediction error. The results showed that the ρdmax, Krd, and K20 all obtained optimal states when fov was set as f0 for different gradation HRGG fillers. Furthermore, it was found that the key features influencing the fov were determined to be the maximum particle diameter dmax, gradation parameters b and m, flat and elongated particles in coarse aggregate Qe, and the Los Angeles abrasion of coarse aggregate LAA. Among them, the influence of dmax on the ANN-PM predictive performance was the most significant. On the training and testing sets, the goodness-of-fit R2 of ANN-PM all exceeded 0.95, and the prediction errors were small, which indicated that the accuracy of ANN-PM predictions was relatively high. In addition, it was clear that the ANN-PM exhibited excellent robust performance. The research results provide a novel method for determining the fov of subgrade fillers and provide theoretical guidance for the intelligent construction of high-speed railway subgrades.

Contact Analysis of CNT-FGM Nanocomposite Using Indentation Contact Model

In the present paper, carbon nanotube (CNT) reinforced functionally graded (FG) material matrix nanocomposite (CNT-FGM) is indented with a rigid conical indenter and the contact behaviour of the CNT-FGM nanocomposite is analyzed in the context of variation in gradation parameter and wall thickness of the CNTs. The study is conducted on the basis of finite element model, which is developed utilizing Ansys Parametric Design Language (APDL) codes in commercial finite element modelling platform, ANSYS. The material model is developed considering the CNTs are uniformly distributed in the graded matrix, whose elastic properties vary along the direction of indentation. The gradation model, corresponding to the matrix material, adopted for the present study is a power law function. In this study, elastically graded material (EGM)matrix CNT reinforced CNT-FGM nanocomposite has been examined, varying the elastic gradation parameter (+2, 0 and -2) of the matrix material. Additionally, effect of the variations in CNT wall thickness are explored, while maintaining other parameter constant in the nanocomposite. A frictionless contact between the CNT-FGM substrate and rigid indenter is simulated for both loading and unloading process. Prior to extracting pertinent results, the current model undergoes validation by comparing its outcomes with those published in the literature. It is observed that the contact force-displacement plots align satisfactorily for both sets of data. Various contact parameters, such as contact force, contact area, contact pressure distribution, stress distribution, and deformation behavior during the indentation contact, are then extracted from the analysis. The observation indicates that positive gradation parameter result in superior contact properties compared to negative gradation parameters as well as without gradation.

Investigation on permeability of filter cake with different particle sizes: Experimental and simulation study

Cake permeability is a crucial macroscopic physical property in characterizing filtration behavior for coal slurry. However, there is still no appropriate model derived to establish the essential relationship between the cake permeability and complex particle property. In this study, the three-dimensional microstructures with different particle gradation and porosity were generated using particle discrete element software. The permeability of each constructed model was calculated utilizing Lattice Boltzmann Method (LBM). Simulation result shows that the Kozeny–Carman (KC) model is proper to predict the permeability of porous media with different gradations. When the gradation parameters are not considered, it shows that d30 can best represent the particle size characteristics of the particle systems. When the porosity and average particle size remain unchanged, the permeability increases with the increasing of the non-uniformity coefficient (Cu) and the curvature coefficient (Cc). Moreover, we proposed an analytical expression for the permeability estimation with different gradation characteristics, which contains the parameters that can be easily measured in engineering. Then a set of coal slurry samples with different particle size distributions were selected to conduct the filtration test, the permeability of each sample was measured to compare with the predicted results of the gradation permeability model. It is clear that the KC equation in this case can not calculate the cake permeability effectively. Although the gradation model is relatively appropriate to estimate the cake permeability, its accuracy still needs to be improved by considering the pore structure property to eliminate the effect of constant terms.

Coefficient of permeability prediction of soils using gene expression programming

The coefficient of permeability is the most important parameter for characterizing the permeability characteristics of soil. In this study, a database of the coefficient of permeability (k) for soils was compiled firstly, including effective size (d10), mean grain size (d50), the particle diameters at the cumulative percent content of 60% (d60) and void ratio (e), which can reflect the impact of the particle size distribution characteristics. Gene expression programming (GEP), was employed to develop a predictive equation of k, which is convenient for engineering application. The GEP model was compared with eight empirical models and other artificial intelligence models (i.e., random forest (RF) and group method of data handling (GMDH)). The results show that the GEP model and RF model have the highest accuracy, followed by GMDH model and empirical models. Then, the sensitivity analysis was conducted. According to the results, k increases with increasing d10, d50 and e; the effective size d10 has the largest influence on k, followed by d50 and e, which indicates that fine particle content has the control effect on seepage and the effect of soil gradation parameters on k is greater than that of void ratio. Additionally, the SHAP and PDP analysis were conducted to investigate the feature importance and conditional expectation of each feature. The feature importance order is d10 > d50 > e > d60, and the SHAP values of d10, d50 and e increase with the increase of them, while the SHAP value of d60 is basically unchanged.

Modelling of bidirectional functionally graded plates with geometric nonlinearity: A comparative dynamic study using whole domain and finite element method

Present work attempts to study the nonlinear dynamic behaviour of bidirectional functionally graded plates (BFG) along with unidirectional functionally graded plates (UFG). For BFG plates the material is graded along the thickness and length simultaneously and two types of UFG plates are considered namely thickness-wise (TFG) and length-wise (AFG) plates. The dynamic problem is formulated using two different methods namely, whole domain method and finite element method. The constitutive relation for the former is generated using classical plate theory whereas Mindlin plate theory is used in the later. Geometric nonlinearity is addressed here by using Von Karman type strain-displacement relation. Both clamped and simply supported boundary conditions are considered and the plate is assumed to be under action of uniformly distributed external load. The results of free and forced vibration analyses are presented as backbone curves and frequency response curves respectively. The results show that all three plates exhibit hardening type nonlinearity. It is also seen that the effect of material gradation parameters is more profound in simply supported plates compared to clamped plates. Specifically, these parameters do not seem to have much effect on BFG clamped plates in free vibration. The outcome of the analysis is compared with those in existing literature and fair agreement is observed between them.

Gradation Influence on Crack Resistance of Stress-Absorbing Membrane Interlayer

Reflective cracking is a common distress of old pavement overlaid with an asphalt layer. The asphalt rubber stress-absorbing membrane interlayer can effectively mitigate and prevent reflective cracking. However, the existing test methods and evaluation indices for the crack resistance of the asphalt rubber stress-absorbing membrane interlayer are insufficient and unsystematic. They do not account for the significant effect of gradation parameters on the crack resistance in a comprehensive way. Therefore, this research aims to explore the impact of gradation parameters on the performance of the asphalt rubber stress-absorbing membrane interlayer. Based on the Chinese and U.S. standards, three kinds of 10 types of gradation were selected, forming a total of seven groups. The asphalt rubber stress-absorbing membrane interlayer was subjected to −10 °C and 15 °C beam bending test, low-temperature semi-circular bend test, crack expansion semi-circular bend test, and overlay test to evaluate its cracking resistance. The correlation and influence law between the key sieve hole method, graded fractal method, and Bayley method parameters of different grades and beam bending test, low-temperature semi-circular bend test, crack expansion semi-circular bend test, and overlay test indexes were quantitatively analyzed by the coefficient of variation and Pearson correlation analysis method. The results showed that the performance of the mixtures with different gradation ranges varied significantly in different tests, as indicated by the maximum difference of 56.07% in stress absorption. This implied that gradation is a critical factor that affects the stress absorption performance of mixes. The different sensitivities of different tests to the parameters of the key sieve method, the graded fractal method, and the Bailey method indicated that the stress absorption performance was affected by a combination of factors. Therefore, in order to evaluate and optimize the stress absorption performance, it was necessary to comprehensively consider the interactions among the parameters of the key sieve method, the graded fractal method, and the Bailey method. The stress absorption performance included crack resistance and crack expansion resistance, which were inversely related and needed to be balanced and optimized during design. The −10 °C beam bending test and crack expansion semi-circular bend tests were more suitable test methods for evaluating stress absorption performance, and maximum flexural–tensile strain, strain energy density, fracture energy, and flexibility index were recommended as evaluation indicators. This research provides a reference for the optimization of the grading design of asphalt rubber stress-absorbing membrane interlayers, and provides test methods and indicators for the evaluation of crack resistance.

Boosting Hot Mix Asphalt Dynamic Modulus Prediction Using Statistical and Machine Learning Regression Modeling Techniques

The prediction of asphalt mixture dynamic modulus (E*) was investigated based on 1128 E* measurements, using three regression and thirteen machine learning models. Asphalt binder properties and mixture volumetrics were characterized using the same feeding features in the NCHRP 1-37A Witczak model. However, three aggregate gradation characterization approaches were involved in both modelling techniques: the NCHRP 1-37A gradation parameters, Weibull distribution factors, and Bailey method parameters. This study evaluated the performance of these models based on various performance indicators, using both statistical and machine learning regression modeling techniques. K-fold cross-validation and learning curve analysis were conducted to assess the models’ generalization capabilities. The conclusions of this study demonstrate the superiority of the ML models, particularly the Catboost ensemble learning regression (CbR). Hyperparameter optimization and residual analysis were performed to fine-tune and confirm the heteroscedasticity of the CbR model. The Bailey-based CbR model showed the highest coefficient of determination (R2) of 0.998 and the lowest root mean square error (RMSE) of 220 MPa. Moreover, SHAP values interpreted the CbR model and showed the relative importance of its feeding features. Based on the findings of this study, the CbR model is suggested to accurately predict E* for a variety of asphalt mixtures. This information can be used to improve pavement design and construction, leading to more durable and long-lasting pavements.

Effect of air voids on the fracture resistance of HMA in the indirect tensile cracking (IDEAL-CT) test

ABSTRACT Air voids (density) is a key volumetric property of asphalt mixes and play an unignorable role in anti-cracking. While lower voids were expected to result in more durable asphalt mixtures, contradictory trends were found in lab testing. In this study, several plant-produced mixtures were compacted to three levels of air voids (7%, 9%, and 11%) and tested to evaluate the sensitivity to the crack resistance. The results indicate that the higher the air voids, the better the cracking resistance as measured by the CTindex. To understand the cause, a stepwise regression analysis was conducted to model the IDEAL-CT parameters with volumetric and gradation parameters. The air void, percent passing the 0.6 mm (P0.6) and binder content (Pb) are found to have significant effects on CTindex and post-peak slope. The analysis of the CT change rate shows that the Pb, dust to binder ratio (D/B), and P0.6 can be used to predict the effect of the voids with a correlation factor (R2) of 0.96. For further understanding, the asphalt film thickness was calculated and found that compared with using film thickness as the single fitting parameter, combining the film thickness with D/B in modelling can dramatically increase the R2 of correlation from 0.67 to 0.92.

Study on Correlations between Tailings Particle Size Distribution and Rheological Properties of Filling Slurries

The influence of the mass concentration and particle size distribution on rheological parameters and slump was investigated by analyzing the mixture of overflow tailings (OT) and classified tailings (CT). The correlation between the rheological parameters, slump and characteristic value of the tailings particle diameter was discussed. Finally, the ratio parameters of a mixed tailings filling slurry were optimized using a lead–zinc mine in Guangdong, China as the engineering background. The results showed a quadratic decrease in the slump of the tailings slurry as the mass concentration increased, while the slump decreased with a decreasing particle size. With the increase in the mass concentration, the yield stress of the tailings slurry follows a quadratic function, while the plastic viscosity exhibits linear growth. The influence of tailings fine particles on the sensitivity of the yield stress and plastic viscosity to the mass concentration is reduced as their content increases. The yield stress and plastic viscosity of the tailings slurry exhibit a quadratic function decrease and linear growth as the slump increases. The slump of the tailings slurry is related to the particle size characteristic value and the mass concentration of the slurry. The correlation coefficient between the yield stress and the aggregate characteristic particle size increases most obviously at d10~d50, and the increase in the characteristic particle size will enhance the correlation between the yield stress and particle gradation. The correlation coefficient between the plastic viscosity and aggregate particle gradation parameters exhibits an initial increase followed by stabilization with an increasing characteristic particle size, with the most significant increase observed at d10~d50.

A novel method for dry density forecasting of high-speed railway graded aggregate fillers based on the PSO-ANN model

To address the lack of intelligent prediction research on dry density, this paper proposes a machine learning-based prediction method for compaction quality of high-speed railway graded aggregate (HRGA) fillers. Firstly, to reveal the main control characteristics between HRGA fillers performance and dry density, 80 sets of vibration compaction tests were carried out. The Grey Relation Analysis (GRA) algorithm was used to quantify the degree of influence between the properties of particle gradation, particle shape and particle fragmentation, and dry density. Then, a stable database of dry density of HRGA fillers was established through 300 lab vibratory compaction tests. Finally, three Hybrid Machine Learning (ML) models (PSO-ANN, PSO-SVR, and PSO-RF) was used to fit the nonlinear relationship between the mian control characteristics and dry density. The results show that the gradation parameter b, maximum particle size dmax, and gradation parameter m are strongly correlated features, with a mean value of correlation reaching 0.77. These three features are considered the main control factors affecting the dry density and are used as input features for the subsequent ML prediction model. Moreover, the PSO-ANN model is shown to have the higher prediction accuracy with R2 = 0.9634 in the test set and lower uncertainty with U95= 0.1413, Tstat= 0.533, and SI = 0.2306 in the test set for dry density predictions. The finding can provide a significant reference for intelligent compaction.

Use of hydraulic radius to estimate the permeability of coarse-grained materials using a new geodatabase

This paper reviews commonly used parameters and prediction models for assessing the permeability of granular soils. Following a review of published models for prediction of soil permeability, a dimensional homogenous transformation model for a-priori estimation of soil permeability was calibrated using a large database (CG/KSAT/7/1278) comprising permeability data for a wide range of granular soils sourced from over 50 publications. The new transformation model requires knowledge of the void ratio and gradation of the material to make estimates of the soil permeability. The prediction accuracy of the calibrated model was then assessed alongside that of other empirical and semi-empirical models also calibrated using CG/KSAT/7/1278. The potential influences of void ratio, key gradation parameters and permeability test type on the prediction accuracy of the proposed model are also examined. The paper shows that while the fitted constants in the proposed transformation model are affected to varying extents by the aforementioned parameters, it does offer reasonable predictions of permeability with only knowledge of the void ratio and material gradation required.

Compression responses and particle breakage of calcareous granular material in reclaimed islands

Calcareous gravelly sand (CGS) is a highly compressible and easily breakable granular geomaterial widely used to fill the island reef foundation. In this study, a series of confined compression tests were conducted on CGS, and the particle breakage of CGS was quantitatively assessed. The results showed that the power function model was more suitable for describing the stress-strain relationship of CGS under confined compression than the hyperbolic and Gunary models. The yield stress of CGS was distributed within the range of 1.89–2.45 MPa, which was significantly smaller than that of terrigenous quartz sand. The compression modulus of CGS increased with increasing relative density (Dr); however, it first increased and then decreased with increasing nonuniform coefficient (Cu). The compression behaviors of CGS after the yield point were mainly determined by particle breakage. The CGS sample, which had experienced more serious particle breakage, was compressed to a more compact state under vertical stress. The relative breakage ratio of CGS decreased with increasing Dr, whereas it first increased and then decreased with increasing Cu. The changes in the fractal dimension and gradation parameters of CGS before and after testing were also evaluated. The particle breakage mechanism of CGS during confined compression was revealed based on the changes in the mass fraction of different particle groups before and after testing.